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| import streamlit as st | |
| import pandas as pd | |
| from os import path | |
| import sys | |
| import streamlit.components.v1 as components | |
| sys.path.append('code/') | |
| #sys.path.append('ASCARIS/code/') | |
| import pdb_featureVector | |
| import alphafold_featureVector | |
| import argparse | |
| from st_aggrid import AgGrid, GridOptionsBuilder, JsCode,GridUpdateMode | |
| showWarningOnDirectExecution = False | |
| def download_button(object_to_download, download_filename): | |
| if isinstance(object_to_download, pd.DataFrame): | |
| object_to_download = object_to_download.to_csv(index=False) | |
| # Try JSON encode for everything else | |
| else: | |
| object_to_download = json.dumps(object_to_download) | |
| try: | |
| # some strings <-> bytes conversions necessary here | |
| b64 = base64.b64encode(object_to_download.encode()).decode() | |
| except AttributeError as e: | |
| b64 = base64.b64encode(object_to_download).decode() | |
| dl_link = f"""<html><head><title>Start Auto Download file</title><script src="http://code.jquery.com/jquery-3.2.1.min.js"></script><script>$('<a href="data:text/csv;base64,{b64}" download="{download_filename}">')[0].click()</script></head></html>""" | |
| return dl_link | |
| def download_df(): | |
| components.html( | |
| download_button(selected_df, st.session_state.filename), | |
| height=0, | |
| ) | |
| original_title = '<p style="font-family:Trebuchet MS; color:#FD7456; font-size: 35px; font-weight:bold; text-align:center">Welcome to ASCARIS</p>' | |
| st.markdown(original_title, unsafe_allow_html=True) | |
| st.write('') | |
| st.write('') | |
| st.write('') | |
| st.write('') | |
| source = st.selectbox('Select Protein Structure Database (1: PDB, SwissModel, Modbase 2: AlphaFold)',[1,2]) | |
| impute = st.selectbox('Select Imputation',[True, False]) | |
| input_data = st.text_input('Enter Input Variation') | |
| #sys.path.append(path.abspath('../code/')) | |
| parser = argparse.ArgumentParser(description='ASCARIS') | |
| parser.add_argument('-s', '--source_option', | |
| help='Selection of input structure data.\n 1: PDB Structures (default), 2: AlphaFold Structures', | |
| default=1) | |
| parser.add_argument('-i', '--input_datapoint', | |
| help='Input file or query datapoint\n Option 1: Comma-separated list of idenfiers (UniProt ID-wt residue-position-mutated residue (e.g. Q9Y4W6-N-432-T or Q9Y4W6-N-432-T, Q9Y4W6-N-432-T)) \n Option 2: Enter comma-separated file path') | |
| parser.add_argument('-impute', '--imputation_state', default='True', | |
| help='Whether resulting feature vector should be imputed or not. Default True.') | |
| args = parser.parse_args() | |
| input_set = input_data | |
| mode = source | |
| impute = impute | |
| print('*****************************************') | |
| print('Feature vector generation is in progress. \nPlease check log file for updates..') | |
| print('*****************************************') | |
| mode = int(mode) | |
| with st.spinner('In progress...This may take a while...'): | |
| try: | |
| if mode == 1: | |
| selected_df = pdb_featureVector.pdb(input_set, mode, impute) | |
| int_builder = GridOptionsBuilder.from_dataframe(selected_df) | |
| int_builder.configure_default_column(editable=False, filterable=True, cellStyle={'text-align': 'center'}) | |
| int_builder.configure_pagination(enabled=True, paginationAutoPageSize=False, paginationPageSize=10) | |
| int_builder.configure_selection(selection_mode='multiple', use_checkbox=True) | |
| gridoptions = int_builder.build() | |
| int_return = AgGrid(selected_df, | |
| width='100%', | |
| height=(len(selected_df) + 4) * 35.2 + 3, | |
| theme='light', | |
| enable_enterprise_modules=False, | |
| gridOptions=gridoptions, | |
| fit_columns_on_grid_load=False, | |
| update_mode=GridUpdateMode.SELECTION_CHANGED, # or MODEL_CHANGED | |
| custom_css={".ag-header-cell-label": {"justify-content": "center"}}) | |
| st.success('Feature vector successfully created.') | |
| elif mode == 2: | |
| selected_df = alphafold_featureVector.alphafold(input_set, mode, impute) | |
| int_builder = GridOptionsBuilder.from_dataframe(selected_df) | |
| int_builder.configure_default_column(editable=False, filterable=True, cellStyle={'text-align': 'center'}) | |
| int_builder.configure_pagination(enabled=True, paginationAutoPageSize=False, paginationPageSize=10) | |
| int_builder.configure_selection(selection_mode='multiple', use_checkbox=True) | |
| gridoptions = int_builder.build() | |
| int_return = AgGrid(selected_df, | |
| width='100%', | |
| height=(len(selected_df) + 4) * 35.2 + 3, | |
| theme='light', | |
| enable_enterprise_modules=False, | |
| gridOptions=gridoptions, | |
| fit_columns_on_grid_load=False, | |
| update_mode=GridUpdateMode.SELECTION_CHANGED, # or MODEL_CHANGED | |
| custom_css={".ag-header-cell-label": {"justify-content": "center"}}) | |
| st.success('Feature vector successfully created.') | |
| except: | |
| pass | |
| download_df = pd.DataFrame() | |
| with st.form("my_form", clear_on_submit=False): | |
| st.text_input("Enter filename", key="filename") | |
| submit = st.form_submit_button("Download feature vector", on_click=download_df) | |